Gross and Applied Anatomy Pedagogical Approaches in Occupational Therapy Education: A Scoping Review
Bibliographic record
Abstract
Introduction. With technological advancements, anatomy teaching approaches in occupational therapy education have expanded. However, uncertainty remains regarding the approaches that best optimize academic and practice outcomes in student occupational therapists (OTs). Purpose. This scoping review mapped the pedagogical approaches used to teach musculoskeletal anatomy to student OTs. Methods. A scoping review was conducted, with a consultation exercise involving Canadian occupational therapy educators. Six databases were searched, with terms related to student OTs, anatomy, and education. Included articles were available in English, full text; featured empirical research of any study design and/or gray literature; featured a pedagogical approach used to teach anatomy; and targeted student OTs with the pedagogies. Results. Twenty-eight reports between 1978 and 2021 were included. Although technology-based pedagogies became more common with time, historically used pedagogies (e.g., lectures and labs) remained prominent and most common. Narrative synthesis regarding the effectiveness of anatomy pedagogical approaches identified five main factors: (a) anatomy competency; (b) teaching method diversity; (c) learner psychological considerations; (d) interprofessional education; and (e) optimal academic outcomes. Implications. This review demonstrates the importance of anatomy knowledge to occupational therapy education and practice. A diversity of pedagogical approaches, with and without technology, may foster better outcomes by addressing diverse learning needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".